Free R Programming Books For Data Science

Developing Data Products In R

This book covers the exciting field of data products. A data product is the output of a data science experiment. In this book we focus on developing data products in R, the most popular language for data scientists. Special emphasis is given to developing Shiny apps. Shiny is a platform for developing web front ends for back end R calculations. Shiny allows users to create polished web apps while only knowing R and a little bit of html. 

After shiny we cover other ways to make reproducible presentations and interactive graphics. In reproducible presentations, we cover Slidify and RStudio's Presenter. In interactive graphics, we demonstrate RCharts, Leaflet, googleVis and plot.ly.

Data Mining Algorithms in R

Most of the data mining literature is too abstract regarding the actual use of the algorithms and parameter tuning is usually a frustrating task. On the other hand, there is a large number of implementations available, such as those in the R project, but their documentation focus mainly on implementation details without providing a good discussion about parameter-related trade-offs associated with each of them.

This Wikibook aims to fill this gap by integrating three pieces of information for each technique: description and rationale, implementation details, and use cases.

A Little Book of R For Time Series

Avril Coghlan

This is a simple introduction to time series analysis using the R statistics software (have you spotted the pattern yet?). It includes instruction on how to read and plot time series, time series decomposition, forecasting, and ARIMA models

A Little Book Of R For Multivariate Analysis

Avril Coghlan

A Little Book of R for Multivariate Analysis is a simple introduction to multivariate analysis using the R statistics software.

It covers topics such as reading and plotting multivariate data, principal components analysis, and linear discriminant analysis.

It's only 49 pages long and you can read it online or download it as a pdf.

Practical Regression and Anova Using R

Julian J. Faraway

The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

This book is not for beginners.  It presumes some knowledge of basic statistical theory and practice, such as statistical inference like estimation, hypothesis testing and confidence intervals. A basic knowledge of data analysis is presumed. Some linear algebra and calculus is also required..

Data analysis And Graphics Using R

John Maindonald and W. John Braun

Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. 

The many worked examples, from real-world research, are accompanied by commentary on what is done and why.  

introduction to statistical thought

Michael Lavine

"Statistical thinking" means a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. 

The book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts. The book is written with the statistical language R embedded throughout.

from algorithms to z-scores: probabilistic and statistical modeling in computer science

Norm Matloff

The materials here form a textbook for a course in mathematical probability and statistics for computer science students.

Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modelling.

an introduction to statistical learning with applications in r

Gareth James, Daniella Witten, Trevor Hastie and Robert Tibshirani

This book provides an introduction to statistical learning methods.

It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.

The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Forecasting Principles and Practice

Rob J. Hyndman and George Athanasopoulos

Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.